research-article

Searching by Talking: Analysis of Voice Queries on Mobile Web Search

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Ido GuyAuthors Info & Claims

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

Pages 35 - 44

https://doi.org/10.1145/2911451.2911525

Published: 07 July 2016 Publication History

Abstract

The growing popularity of mobile search and the advancement in voice recognition technologies have opened the door for web search users to speak their queries, rather than type them. While this kind of voice search is still in its infancy, it is gradually becoming more widespread. In this paper, we examine the logs of a commercial search engine's mobile interface, and compare the spoken queries to the typed-in queries. We place special emphasis on the semantic and syntactic characteristics of the two types of queries. %Our analysis suggests that voice queries focus more on audio-visual content and question answering, and less on social networking and adult domains. We also conduct an empirical evaluation showing that the language of voice queries is closer to natural language than typed queries. Our analysis reveals further differences between voice and text search, which have implications for the design of future voice-enabled search tools.

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cover image ACM Conferences

SIGIR '16: Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval

July 2016

1296 pages

ISBN:9781450340694

DOI:10.1145/2911451

General Chairs:
Raffaele Perego
ISTI-CNR, Italy
,
Fabrizio Sebastiani
Qatar Computing Research Institute, HBKU, Qatar
,
Program Chairs:
Javed Aslam
Northeastern University, US
,
Ian Ruthven
University of Strathclyde, UK
,
Justin Zobel
University of Melbourne, Australia

Copyright © 2016 ACM.

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Published: 07 July 2016

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SIGIR '16

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SIGIR '16: The 39th International ACM SIGIR conference on research and development in Information Retrieval

July 17 - 21, 2016

Pisa, Italy

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SIGIR '16 Paper Acceptance Rate 62 of 341 submissions, 18%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

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https://doi.org/10.3390/bs14110989
Kashyap NSebastian ALynch CJansons PMaddison RDingler TOldenburg B(2024)Engagement With Conversational Agent–Enabled Interventions in Cardiometabolic Disease Management: Protocol for a Systematic ReviewJMIR Research Protocols10.2196/5297313(e52973)Online publication date: 7-Aug-2024
https://doi.org/10.2196/52973
Trippas JGallagher LMackenzie JSerra ESpezzano F(2024)Re-evaluating the Command-and-Control Paradigm in Conversational Search InteractionsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679588(2260-2270)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679588
Trippas JAl Lawati SMackenzie JGallagher LHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)What do Users Really Ask Large Language Models? An Initial Log Analysis of Google Bard Interactions in the WildProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657914(2703-2707)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657914
Zulfikar WChan SMaes P(2024)Memoro: Using Large Language Models to Realize a Concise Interface for Real-Time Memory AugmentationProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642450(1-18)Online publication date: 11-May-2024
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Ghosh SGhosh SShah CKlein MBen-David AJäschke RKelly M(2024)Toward Connecting Speech Acts and Search Actions in Conversational Search TasksProceedings of the 2023 ACM/IEEE Joint Conference on Digital Libraries10.1109/JCDL57899.2023.00027(119-131)Online publication date: 26-Jun-2024
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Pucciarelli FKaplan A(2024)Voice-Powered Artificial IntelligenceThe Cambridge Handbook of Cyber Behavior10.1017/9781107165250.018(438-460)Online publication date: 6-Dec-2024
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Saeed ZAslam FGhafoor AUmair MRazzak I(2024)Exploring the impact of SEO-based ranking factors for voice queries through machine learningArtificial Intelligence Review10.1007/s10462-024-10780-957:6Online publication date: 16-May-2024
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Lal MNeduncheliyan S(2024)The Evolution and Potential of Conversational Agents in HealthcareMachine Learning Algorithms10.1007/978-3-031-75861-4_18(209-220)Online publication date: 12-Nov-2024
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